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Documentation Index

Fetch the complete documentation index at: https://docs.xhipai.com/llms.txt

Use this file to discover all available pages before exploring further.

Embedding providers convert text into high-dimensional vectors for semantic search. Agentium provides a unified EmbeddingProvider interface with OpenAI and Google implementations.

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EmbeddingProvider Interface

interface EmbeddingProvider {
  embed(text: string): Promise<number[]>;
  embedBatch(texts: string[]): Promise<number[][]>;
}

​
OpenAI Embeddings

npm install openai

import { OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding({
  apiKey: process.env.OPENAI_API_KEY,  // optional, uses env var by default
  model: "text-embedding-3-small",      // optional, this is the default
});

const vector = await embedder.embed("Hello world");
console.log(vector.length); // 1536

const vectors = await embedder.embedBatch(["Hello", "World"]);
console.log(vectors.length); // 2

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Available Models

ModelDimensionsBest For
text-embedding-3-small1536General use, cost-effective
text-embedding-3-large3072Higher accuracy
text-embedding-ada-0021536Legacy
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apiKey
string
OpenAI API key. Falls back to OPENAI_API_KEY env var.
​
model
string
default:"text-embedding-3-small"
Embedding model name.

​
Google Embeddings

npm install @google/genai

import { GoogleEmbedding } from "@agentium/core";

const embedder = new GoogleEmbedding({
  apiKey: process.env.GOOGLE_API_KEY,  // optional, uses env var by default
  model: "text-embedding-004",         // optional, this is the default
});

const vector = await embedder.embed("Hello world");
const vectors = await embedder.embedBatch(["Hello", "World"]);

​
Available Models

ModelDimensionsBest For
text-embedding-004768General use
embedding-001768Legacy
​
apiKey
string
Google API key. Falls back to GOOGLE_API_KEY env var.
​
model
string
default:"text-embedding-004"
Embedding model name.

​
Using with KnowledgeBase

Embedding providers are passed to KnowledgeBase via the vector store. Most vector stores accept an EmbeddingProvider in their configuration or the KnowledgeBase handles embedding internally. 
import { KnowledgeBase, InMemoryVectorStore, OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding();
const vectorStore = new InMemoryVectorStore(1536);

const kb = new KnowledgeBase({
  name: "docs",
  vectorStore,
});

RAG Example

See a complete end-to-end RAG implementation using embeddings.